Simulation has been applied with success in a large number of works. For example, in Gatti, M. A. C., A. P. Appel, C. N. Santos, C. S. Pinhanez, P. R. Cavalin, and S. M. B. Neto. “A Simulation-based Approach to Analyze the Information Diffusion in Microblogging Online Social Network” (Proc. of the Winter Simulation Conference, WSC'13, Berlin, 2013), a stochastic multi-agent based simulation (SMSim) is used where each agent represents a user in a sampled egocentric social network. In the SMSim simulator each agent encapsulates the behavior of a social media network user. The environment where the agents operate and interact is the followers graph extracted from the social media network. The SMSim is modeled as a discrete-event simulation where the operation of the system is represented as a chronological sequence of events. An agent's basic actions in the simulator are To Read or To Post and its states are Idle or Posting and in both states the agent reads the received messages from whom he or she follows and posting or not depending on the modeled behavior. When the agent is posting a message, at the simulator level, it is sending the message to all its followers. The message can have a number of features, for instance, a positive or negative sentiment about a topic. Each agent behavior is determined by a Markov Chain Monte Carlo simulation method where the Markov Chain transitions probabilities are estimated from the sampling data. When the SMSim is started, each agent switches its behavior to Posting or Idle depending on the activated transitions using a Monte Carlo method. The transition will only be activated if the probability value calculated corresponds to a random value generated by the system. If no transition is activated, the system switches the user's state to Idle. Some experiments were run in the simulator to evaluate the effect of removing the most engaged users, aiming to find those that have the most effect on the information flow. Visual analytics on time series had to be performed in order to observe the effect. Removing the top 100 most engaged users had more effect than removing the seed (as used here, the observed effect means that it consistently effects the number of messages sent by the users over time). However, visual analytics typically does not scale nor enable automatic optimization of planning by parameter tuning on the users' behavior. To summarize the above, a social network is used as input for the model acting in the simulation (and in this case, the agents will represent the user and his/her followers (or friends or connections) in the social network; in addition, these agents simulate how messages are exchanged between these users).
Other techniques include: pattern recognition; time-series analysis (to predict outliers); linear models; non-linear least squares method; outlier detection; step detection problem (signal processing, digital image processing, noise reduction); social network analysis; and various methods to find “who” and/or “when” but not “how long” all together.
In any case, for the study of information diffusion in large Online Social Networks (OSNs) there is still a lack of work.
Accordingly, as described herein the present disclosure provides a mechanism which scales and enables enable automatic optimization of planning by parameter tuning on the users' behavior.
Further, as described herein the present disclosure provides a mechanism which relies on using a real OSN to simulate the users' behavior based on what they post in order to analyze how the information is spread across a network.
In various embodiments, such disclosed mechanisms may be implemented via systems, methods and/or computer program products.